activity
20222025
most citedMachine learning classification of CHIME fast radio bursts -- I. Supervised methods

42 citations · 86 across the 5 of their papers we have counts for

collaborators

5 papers

astro-ph.CO2025★ 6 cited

Hubble constant constraint using 117 FRBs with a more accurate probability density function for

Jiaming Zhuge, Marios Kalomenopoulos, Bing Zhang

Fast radio bursts (FRBs) are among the most mysterious astronomical transients. Due to their short durations and cosmological distances, their dispersion measure (DM) - redshift ($…

astro-ph.HE2024

Separating repeating fast radio bursts using the minimum spanning tree as an unsupervised methodology

C. R. García, Diego F. Torres, Jia-Ming Zhu-Ge +1

Fast radio bursts (FRBs) represent one of the most intriguing phenomena in modern astrophysics. However, their classification into repeaters and non-repeaters is challenging. Here,…

astro-ph.HE2022★ 42 cited

Machine learning classification of CHIME fast radio bursts -- I. Supervised methods

Jia-Wei Luo, Jia-Ming Zhu-Ge, Bing Zhang

Observationally, the mysterious fast radio bursts (FRBs) are classified as repeating ones and apparently non-repeating ones. While repeating FRBs cannot be classified into the non-…

astro-ph.HE2022★ 2 cited

Identifying the physical origin of gamma-ray bursts with supervised machine learning

Jia-Wei Luo, Fei-Fei Wang, Jia-Ming Zhu-Ge +3

The empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally…

astro-ph.HE2022★ 36 cited

Machine learning classification of CHIME fast radio bursts: II. Unsupervised Methods

Jia-Ming Zhu-Ge, Jia-Wei Luo, Bing Zhang

Fast radio bursts (FRBs) are one of the most mysterious astronomical transients. Observationally, they can be classified into repeaters and apparently non-repeaters. However, due t…